Action selection in continuous state and action spaces by cooperation and competition of extended kohonen maps

Authors:
Kian Hsiang Low;Wee Kheng Leow;Marcelo H. Ang, Jr.
Affiliations:
National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore
Venue:
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Year:
2003

Citing 2
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Abstract

This paper presents an action selection framework based on an assemblage of self-organizing neural networks called Cooperative Extended Kohonen Maps. This framework encapsulates two features that significantly enhance a robot's action selection capability: self-organization in the continuous state and action spaces to provide smooth, efficient and fine motion control; action selection via the cooperation and competition of Extended Kohonen Maps to achieve more complex motion tasks. Qualitative tests demonstrate the capability of our action selection method for both single- and multi-robot motion tasks.